Nonlinear Regression Analysis Research Articles

In Vivo Bioluminescence Imaging Reveals Differences in Leishmania infantum Parasite Killing Kinetics by Antileishmanial Reference Drugs.

The bioluminescent Leishmania infantum BALB/c mouse model was used to evaluate the parasiticidal drug action kinetics of the reference drugs miltefosine, paromomycin, sodium stibogluconate, and liposomal amphotericin B. Infected mice were treated for 5 days starting from 7 days post-infection, and parasite burdens were monitored over time via bioluminescence imaging (BLI). Using nonlinear regression analyses of the BLI signal, the parasite elimination half-life (t1/2) in the liver, bone marrow, and whole body was determined and compared for the different treatment regimens. Significant differences in parasiticidal kinetics were recorded. A single intravenous dose of 0.5 mg/kg liposomal amphotericin B was the fastest acting with a t1/2 of less than 1 day. Intraperitoneal injection of paromomycin at 320 mg/kg for 5 days proved to be the slowest with a t1/2 of about 5 days in the liver and 16 days in the bone marrow. To conclude, evaluation of the cidal kinetics of the different antileishmanial reference drugs revealed striking differences in their parasite elimination half-lives. This BLI approach also enables an in-depth pharmacodynamic comparison between novel drug leads and may constitute an essential tool for the design of potential drug combinations.

Experimental research on the impact pressure of tidal bore fronts.

Tidal bore impact can be strong and destructive, placing estuarine infrastructures under great threat. However, there is a lack of research focusing on accurately estimating the impact pressure exerted by tidal bores. Herein new experiments were conducted to investigate the pressure of tidal bore fronts in a glass flume. Through analysis of instantaneous pressure of three forms of tidal bore, it was observed that the pressure fluctuation of weak and strong breaking bore fronts is characterized by impact pressure. The vertical distribution and maximum impact pressure of tidal bore were studied.The maximum impact pressure of breaking bore fronts appeared around 0.46 times height of it. The relationship between relative impact pressure and height of the tidal bore fronts was found to closely follow a normal probability density function. Through nonlinear regression analysis, an empirical equation was derived to calculate impact pressure, which was validated using observation data from the Qiantang River in China. This equation can be utilized to predict the impact pressure of tidal bore fronts and provide valuable support for estuarine engineering design.

Effects of adenoid hypertrophy on nasopharyngeal airway ventilation: A computational fluid dynamics study

Adenoid hypertrophy causes impaired nasopharyngeal airways (NA) ventilation. However, it is difficult to evaluate the ventilatory conditions of NA. Therefore, this study aimed to analyze the nasopharyngeal airway resistance (NARES) based on computational fluid dynamics simulations and the nasopharyngeal airway depth (NAD) and adenoid hypertrophy grade measured on cephalometric cone-beam computed tomography images and determine the relationship between NAD and grade and NARES to ultimately assess using cephalometric measurements whether NA has airway obstruction defects. Cephalogram images were generated from cone-beam computed tomography data of 102 children (41 boys; mean age: 9.14±1.43 years) who received orthodontic examinations at an orthodontic clinic from September 2012 to March 2023, and NAD and adenoid grade and NARES values were measured based on computational fluid dynamics analyses using a 3D NA model. Nonlinear regression analyses were used to evaluate the relationship between NARES and NAD and correlation coefficients to evaluate the relationship between grade and NARES. NARES was inversely proportional to the cube of NAD (R2=0.786, P<0.001), indicating a significant relationship between these variables. The resistance NARES increased substantially when the distance NAD was less than 5mm. However, adenoid Grade 4 (75% hypertrophy) was widely distributed. These study findings demonstrate that the ventilatory conditions of NA can be determined based on a simple evaluation of cephalogram images. An NAD of less than 5mm on cephalometric images results in NA obstruction with substantially increased airflow resistance.

Facile compliance-based pump for blood physiometer

The biomechanical properties of blood are considered promising label-free biomarkers for early disease detection. Disposable pumps have been suggested as replacements for bulky and expensive syringe pumps. However, they have limitations, including initial air bubble removal, simple stop-and-run flow control, and quantification of many rheological properties. In this study, a compliance-based pump (CP) is developed by fitting a blood-loaded syringe, an air-compliance unit, and a needle into each port of a three-way valve. When blood is loaded into the microfluidic channel from the CP, the initial air bubbles are removed from the channels. By manipulating the three-way valve, blood flow is stopped immediately. Red blood cell (RBC) aggregation index (AI) is obtained by analyzing microscopic blood images. The air-compliance unit induces a transient blood flow. The time-resolved micro-particle image velocimetry technique is employed to obtain the blood velocity. The flow rate and time constant are obtained by assuming the flow rate as Q (t) = Q1 exp(−t/λ1) + Q2 exp(t/λ2) and conducting nonlinear regression analysis. The proposed method is employed to obtain the properties (AI, Q1, Q2, λ1, and λ2) of suspended bloods (hematocrit = 30%–70%, 5–30 mg/ml dextran solution, and heat-shocked RBCs). It is also used to detect four different types of suspended blood prepared by adding two types of RBCs (normal or hardened RBCs) to two types of diluents (1 × phosphate-buffered saline or dextran). In conclusion, the proposed method can be used to detect differences in suspended blood by manipulating the CP and consistently analyzing microscopic blood images.

Numerical Simulation Study on Blasting-induced Damage Characteristics of Frozen Rock

Rock masses in cold regions are exposed to a negative temperature environment for extended periods, causing significant changes in their mechanical properties. This inevitably results in substantial differences in the blasting characteristics compared to rocks at normal temperatures. To reveal the influence of different temperatures on the blasting characteristics of frozen rocks, this paper utilized a finite element method-based LS-DYNA software for numerical simulation of single borehole blasting at freezing temperatures of -5°C, -10°C, -20°C, and -30°C. The results showed that under negative temperature conditions, the degree of rock blasting-induced damage gradually decreased as the rock temperature decreased. As the temperature decreased from -5°C to -30°C, the volume of rock blasting-induced damage decreased by approximately 14%. Additionally, this paper has further investigated the effects of ignition locations, free surface, and borehole burdens on the blasting-induced damage characteristics of frozen rock. It was found that the volume of the blast cavity is the largest in reverse detonation. While forward detonation is more favorable for damaging the top rock and helps to improve the rock fragmentation at the orifice. Increasing the free surface can significantly improve the effect of blasting-induced damage. There is an optimal borehole burden distance, which can fully utilize the explosive energy and break the rock efficiently. Finally, based on multivariate nonlinear regression analysis, an empirical formula of the reduction coefficient of the specific charge for frozen rock mass is developed, which can provide a theoretical basis for designing and optimizing rock engineering blasting in alpine and cold regions.

Machine learning for fatigue lifetime predictions in 3D-printed polylactic acid biomaterials based on interpretable extreme gradient boosting model

Modeling of the fatigue lifetimes in 3D-printed biomaterials of Polylactic acid (PLA) is presented in this article based on machine learning (ML) techniques of interpretable extreme gradient boosting (XGBoost) and Shapley additive explanations. For this objective, standard testing samples were additive-manufactured from PLA under different 3D printing parameters. Then, the fatigue experiments were performed on specimens under various stress levels. Based on these data, three ML methods were utilized for modeling the PLA fatigue lifetimes, including XGBoost, random forest, and support vector regression, besides a common nonlinear regression analysis. The obtained results indicated that XGBoost had superior modeling results, compared to other ML techniques and the regression analysis. The coefficient of determination was 97.66 % with a scatter-band of ±1.3, which was a narrow scatter in fatigue modeling.

Understanding Plugging Agent Emplacement Depth with Polymer Shear Thinning: Insights from Experiments and Numerical Modeling

Polymer-plugging agents are widely employed in profile control and water-plugging measures, serving as a crucial component for efficient reservoir development. However, quantitatively monitoring the emplacement depth of polymer-plugging agents in low-permeability and high-permeability layers remains a challenging bottleneck. Presently, insufficient attention on shear thinning, a critical rheological property for water shut-off and profile control, has limited our understanding of polymer distribution laws. In this study, polymer shear-thinning experiments are firstly conducted to explore polymer variations with flow rate. The novelty of the research is that varying polymer viscosity is implemented instead of the fixed-fluid viscosity that is conventionally used. The fitted correlation is then integrated into the 2D and 3D heterogeneous numerical models for simulations, and a multivariate nonlinear regression analysis is performed based on the simulation results. The results show that lower polymer emplacement depth ratios corresponded to higher viscosity loss rates under the same flow rate. An increase in the initial permeability ratio corresponds to a decrease in the emplacement ratio, along with a reduction in the fraction of the plugging agent penetrating the low permeability formations. The model was applied to the Kunan Oilfield and demonstrated a polymer reduction of approximately 3000 tons compared to traditional methods. Despite the slightly complex nature of the multivariate nonlinear mathematical model, it presents clear advantages in controlling plugging agent distribution and estimating dosage, laying good theoretical ground for the effective and efficient recovery of subsurface resources.

Experimental Study on Enhanced Methane Detection Using an MEMS-Pyroelectric Sensor Integrated with a Wavelet Algorithm.

An optical sensing approach that balances portability with cost efficiency has been designed for the reliable monitoring of fugitive methane (CH4) emissions. Employing a LiTaO3-based pyroelectric detector integrated with micro-electro-mechanical systems and a broad infrared source, the developed gas sensor adeptly measured CH4 concentrations with a low limit of detection of about 5.6 ppmv and showed rapid response times with t90 consistently under 3 s. Notably, the novelty of our method lies in its precise control and reduction of CH4 levels, enhanced by wavelet denoising. This technique, optimized through meticulous grid search, effectively mitigated noise interference noticeable at CH4 levels below 10 ppmv. Postdenoising, nonlinear regression analyses based on the modified Beer-Lambert equation returned R2 values of 0.985 and 0.982 for the training and validation sets, respectively. In conclusion, this gas sensor has been shown to be able to meet the requirements for early warning of CH4 leakage on the surface in various carbon capture, utilization, and storage projects such as enhanced oil or gas recovery projects using CO2 injection.

Excess properties and intermolecular interactions of 2-methoxyethanol + ethylenediamine binary system: Density, viscosity, spectral analyses, computational chemistry, and CO2 absorption property

To examine the fundamental physicochemical properties as well as intermolecular interactions for the 2-methoxyethanol (EGME) (1) + ethylenediamine (EDA) (2) binary system, this work systemically measured the density (ρ) and viscosity (η) values of the binary system with various mole ratios at P = 100.5 kPa and T = (298.15–318.15) K with the growth gap of 5 K. The as-measured values of the pure substances were compared with literature data. Multiple semi-empirical formulas were used to fit ρ and η values, and the absolute average deviations (AAD%) were calculated. After that, the excess molar volume (VmE), partial molar volume (V¯), apparent molar volume (Vφ), viscosity deviation (Δη), excess activation of Gibbs free energy (ΔG∗E), and several thermodynamic properties of the binary system were systemically analyzed. According to analysis results, it has been proven that the interaction exists between EGME and EDA molecules. And then, excess properties were fitted to the Redlich-Kister equation using multi-parametric nonlinear regression analyses, and standard deviations (σ) were calculated. In addition, based on various characterization methods including Raman, ultraviolet (UV), and nuclear magnetic resonance hydrogen (1H NMR) spectral analyses and density functional theory (DFT) calculation results, it is demonstrated that there is intermolecular hydrogen bonds in EGME (1) + EDA (2) binary system as the form of –OH⋯NH2–, which was consistent with the existence forms of intermolecular hydrogen bonds in different alcohol-amine systems from references. Finally, CO2 absorption capacity by pure EDA, pure EGME, and the EGME (1) + EDA (2) binary system were severally determined.

Study on the Improvement of Droplet Penetration Effect by Nozzle Tilt Angle under the Influence of Orthogonal Side Wind.

This study investigates the impact of varying side wind velocities and nozzle inclination angles on droplet penetration during plant protection spraying operations, focusing on citrus trees. Experiments were conducted across four wind speed levels (0, 1, 2, 3 m/s) perpendicular to the nozzle direction and seven nozzle inclination levels (0°, 8°, 15°, 23°, 30°, 38°, 45°) to evaluate droplet distribution under different spraying parameters. A baseline condition with 0 m/s wind speed and a 0° nozzle angle served as the control. Utilizing Computational Fluid Dynamics (CFD) and regression analysis techniques in conjunction with field trials, the droplet penetration was analyzed. Results indicate that at constant wind speeds, adjusting the nozzle inclination angle against the direction of the side wind can significantly enhance droplet deposition in the canopy, with a 23° inclination providing the optimal increase in deposition volume, averaging a change of +16.705 μL/cm2. Multivariate nonlinear regression analysis revealed that both wind speed and nozzle inclination angle significantly affect the droplet penetration ratio, demonstrating a correlation between these factors, with wind speed exerting a greater impact than nozzle angle. Increasing the nozzle inclination angle at higher wind speeds improves the penetration ratio, with the optimal parameters being a 23° angle and 3 m/s wind speed, showing a 12.6% improvement over the control. The model fitted for the impact of nozzle angle and wind speed on droplet penetration was validated through field experiments, identifying optimal angles for enhancing penetration at wind speeds of 1, 2, and 3 m/s as 8°, 17°, and 25°, respectively. This research provides insights for improving droplet penetration techniques in plant protection operations.

Comparative analysis of CO2 and N2 adsorption on zeolite 4A: Thermodynamics parameters and site energy distribution investigations

Adsorption equilibrium data for CO2 and N2 on zeolite 4A were volumetrically measured at temperatures ranging from 278 K to 328 K and gas pressures up to 4 MPa. The experimental data was correlated using the high-pressure Langmuir, Sips, and DA adsorption isotherm models. The high-pressure DA model was found to be the optimal model for describing the adsorption isotherms on basis of nonlinear regression analysis. Subsequently, the thermodynamic parameters, including isosteric heat of adsorption (Hst), adsorption phase specific heat capacity (cpa), adsorption phase entropy (sa), and enthalpy (ha), considering the actual adsorption state, were estimated. The results demonstrate that the adsorption of CO2 on zeolite 4A is greater than that of N2. The higher Hst and ha values and the lower sa values of CO2 compared to N2, suggest a stronger surface interaction of CO2 with zeolite 4A, resulting in a more ordered arrangement of CO2 molecules than N2. Site energy distribution theory analysis indicates that less energy is required for CO2 adsorption and greater site energy heterogeneity of CO2 than those of N2 on zeolite 4A. The obtained results evidence that the studied zeolite 4A could be a promising candidate adsorbent for greenhouse gas capture.

Computational Fluid Dynamics Analysis and Empirical Evaluation of Carboxymethylcellulose/Alginate 3D Bioprinting Inks for Screw-Based Microextrusion.

Three-dimensional microextrusion bioprinting technology uses pneumatics, pistons, or screws to transfer and extrude bioinks containing biomaterials and cells to print biological tissues and organs. Computational fluid dynamics (CFD) analysis can simulate the flow characteristics of bioinks in a control volume, and the effect on cell viability can be predicted by calculating the physical quantities. In this study, we developed an analysis system to predict the effect of a screw-based dispenser system (SDS) on cell viability in bioinks through rheological and CFD analyses. Furthermore, carboxymethylcellulose/alginate-based bioinks were used for the empirical evaluation of high-viscous bioinks. The viscosity of bioinks was determined by rheological measurement, and the viscosity coefficient for the CFD analysis was derived from a correlation equation by non-linear regression analysis. The mass flow rate derived from the analysis was successfully validated by comparison with that from the empirical evaluation. Finally, the cell viability was confirmed after bioprinting with bioinks containing C2C12 cells, suggesting that the developed SDS may be suitable for application in the field of bioengineering. Consequently, the developed bioink analysis system is applicable to a wide range of systems and materials, contributing to time and cost savings in the bioengineering industry.

Lubricated Wear Properties of Zn-30Al-3Cu-Si Alloys

AbstractTo assess the effect of silicon content (SC) and test parameters on the lubricated wear of Zn-30 wt.% Al-3 wt.% Cu-(0.5–5) wt.% Si alloys, six quaternary alloys were produced by casting. Their wear properties were investigated at different ranges of contact pressure (CP) of 1–8 MPa and sliding velocity (SV) of 1–3 ms−1 for two different sliding distances (SD) of 20 km and 108 km. It was observed that Si addition (SA) improved the tribological properties of Zn-30Al-3Cu alloy, but an increase in the SC above a certain percentage (0.5%) adversely affected these properties. Therefore, Zn-30Al-3Cu-0.5Si alloy demonstrated the lowest friction coefficient (FC) and wear volume (WV) among the alloys tested. The correlations between the lubricated wear properties (FC and WV) of the alloys and test conditions (CP and SV) were made by non-linear regression analysis using the wear test data. Finally, the equations for the relationship between the wear properties of the alloys and the test parameters were determined.

Energy-Saving Data Acquisition Model of Wireless Sensor Network Based on Nonlinear Algorithm

Wireless sensor network significant data acquisition has a high cost, long completion time, and low accuracy. This paper adopts a sensor network data acquisition method based on a nonlinear algorithm to solve the above problems. In this paper, a distributed data acquisition method based on nonlinear regression is established by combining the time series relationship of data. First, this paper uses nonlinear regression analysis technology to establish the sensing data model and retain the characteristics of the sensing data. This makes the node pass only the parametric data of the regression model. This paper uses this method to replace the transmission of actual monitoring sensory data information. Experiments show that this method can effectively reduce data redundancy and network traffic under any conditions. The method has been verified in practical WSN applications. 

Institutional overlap and the survival of intergovernmental organisations

A burgeoning literature emphasises the role of favourable external conditions and institutional design as key drivers of the survival of intergovernmental organisations. This article focuses on institutional overlap as a hitherto overlooked determinant of IGO survival. Studies on institutional complexity posit that overlap – the extent to which organisations perform similar tasks to address similar problems for some common members – may decrease the survivability of IGOs due to rule conflicts, institutional paralysis, and competitive pressures for limited resources. Contrary to this perspective, this article argues that greater overlap increases the likelihood of IGO survival for two reasons. First, similar IGOs will survive as member states use them to pursue forum-shopping strategies. Second, overlap enables IGO secretariats to draw on networks of support from other IGOs, rendering organisations that densely overlap with others more resilient. To test these propositions, the article combines data on institutional overlap in global governance and the survival of the 534 IGOs contained in the Correlates of War IGO dataset. Non-linear regression analysis finds that where organisations overlap more with existing organisations in terms of governance tasks and issue areas, the likelihood of IGO survival increases. Further analysis suggests that this result is driven by the forum-shopping strategies of powerful member states. Given that contemporary world politics is governed by an increasingly dense network of IGOs, these results hold important implications for the study of IGO survivability and the evolution of global governance more broadly.

Monotonic testing of single shear-plane CLT-to-CLT joint with hardwood dowels

Reducing embodied carbon emissions in buildings and striving for carbon neutrality through sustainable design has become a primary goal in the construction industry. The industry is expanding its use of wood-engineered products, such as cross-laminated timber (CLT) to achieve those sustainability goals. Furthermore, replacing steel fasteners with lower embodied carbon alternatives is gaining increased attention in timber engineering. This paper aims to quantify the mechanical behavior of single shear-plane CLT-to-CLT joints using hardwood dowels. The study includes 154 single shear-plane experimental tests with four different CLT species, two hardwood dowel species, and two dowel diameters. The test specimens were subjected to cyclic and monotonic loading until failure, and mechanical properties such as yielding and ultimate strength, serviceability and yielding stiffness, and ductility were quantified. The hardwood dowel CLT-to-CLT joints demonstrated high strength with up to 10.3 kN (2.3 kips) capacity per dowel and lateral stiffness of up to 5.2 kN/mm (30.8 k/in) per dowel. This strength and stiffness are roughly equivalent to or exceed values for mass timber wood screws, making hardwood dowels a good alternative. In addition, the observed mean ductility of joints ranged from 2.4 to 4.5. Finally, analytical equations for the joints' yielding strength, lateral serviceability stiffness, and yielding stiffness were derived with non-linear regression analysis and validated using the experimental test results.

Understanding optimal cadence dynamics: a systematic analysis of the power-velocity relationship in track cyclists with increasing exercise intensity.

Background: This study aimed to investigate the changes in force-velocity (F/v) and power-velocity (P/v) relationships with increasing work rate up to maximal oxygen uptake and to assess the resulting alterations in optimal cadence, particularly at characteristic metabolic states. Methods: Fourteen professional track cyclists (9 sprinters, 5 endurance athletes) performed submaximal incremental tests, high-intensity cycling trials, and maximal sprints at varied cadences (60, 90, 120rpm) on an SRM bicycle ergometer. Linear and non-linear regression analyses were used to assess the relationship between heart rate, oxygen uptake (V.O2), blood lactate concentration and power output at each pedaling rate. Work rates linked to various cardiopulmonary and metabolic states, including lactate threshold (LT1), maximal fat combustion (FATmax), maximal lactate steady-state (MLSS) and maximal oxygen uptake (V.O2max), were determined using cadence-specific inverse functions. These data were used to calculate state-specific force-velocity (F/v) and power-velocity (P/v) profiles, from which state-specific optimal cadences were derived. Additionally, fatigue-free profiles were generated from sprint data to illustrate the entire F/v and P/v continuum. Results: HR, V.O2 demonstrated linear relationships, while BLC exhibited an exponential relationship with work rate, influenced by cadence (p < 0.05, η2 ≥ 0.655). Optimal cadence increased sigmoidally across all parameters, ranging from 66.18 ± 3.00rpm at LT1, 76.01 ± 3.36rpm at FATmax, 82.24 ± 2.59rpm at MLSS, culminating at 84.49 ± 2.66rpm at V.O2max (p < 0.01, η2 = 0.936). A fatigue-free optimal cadence of 135 ± 11rpm was identified. Sprinters and endurance athletes showed no differences in optimal cadences, except for the fatigue-free optimum (p < 0.001, d = 2.215). Conclusion: Optimal cadence increases sigmoidally with exercise intensity up to maximal aerobic power, irrespective of the athlete's physical condition or discipline. Threshold-specific changes in optimal cadence suggest a shift in muscle fiber type recruitment toward faster types beyond these thresholds. Moreover, the results indicate the need to integrate movement velocity into Henneman's hierarchical size principle and the critical power curve. Consequently, intensity zones should be presented as a function of movement velocity rather than in absolute terms.

A NONLINEAR REGRESSION MODEL FOR EARLY LOC ESTIMATION OF OPEN-SOURCE KOTLIN-BASED APPLICATIONS

Context. The early lines of code (LOC) estimation in software projects holds significant importance, as it directly influences the prediction of development effort, covering a spectrum of different programming languages, and open-source Kotlin-based applications in particular. The object of the study is the process of early LOC estimation of open-source Kotlin-based apps. The subject of the study is the nonlinear regression models for early LOC estimation of open-source Kotlin-based apps. Objective. The goal of the work is to build the nonlinear regression model with three predictors for early LOC estimation of open-source Kotlin-based apps based on the Box-Cox four-variate normalizing transformation to increase the confidence in early LOC estimation of these apps. Method. For early LOC estimation in open-source Kotlin-based apps, the model, confidence, and prediction intervals of nonlinear regression were constructed using the Box-Cox four-variate normalizing transformation and specialized techniques. These techniques, relying on multiple nonlinear regression analyses incorporating multivariate normalizing transformations, account for the dependencies between variables in non-Gaussian data scenarios. As a result, this method tends to reduce the mean magnitude of relative error (MMRE) and narrow confidence and prediction intervals compared to models utilizing univariate normalizing transformations. Results. An analysis has been carried out to compare the constructed model with nonlinear regression models employing decimal logarithm and Box-Cox univariate transformation. Conclusions. The nonlinear regression model with three predictors for early LOC estimation of open-source Kotlin-based apps is constructed using the Box-Cox four-variate transformation. Compared to the other nonlinear regression models, this model demonstrates a larger multiple coefficient of determination, a smaller value of the MMRE, and narrower confidence and prediction intervals. The prospects for further research may include the application of other data sets to construct the nonlinear regression model for early LOC estimation of open-source Kotlin-based apps for other restrictions on predictors.

Comprehensive Empirical Modeling of Shear Strength Prediction in Reinforced Concrete Deep Beams

This paper presents comprehensive empirical equations to predict the shear strength capacity of reinforced concrete deep beams, with a focus on improving the accuracy of existing codes. Analyzing 198 deep beams imported from 15 existing investigations, this study considers various parameters such as concrete compressive strength (f′c), the shear span-to-effective depth ratio (av/d), and reinforcement ratios (ps, pv, and ph). Introducing a novel predictive empirical equation, this study conducts a rigorous evaluation using statistical metrics and a linear regression analysis (MAE, RMSE, and R2). The proposed model demonstrates a significant reduction in the coefficient of variation (CV) to 27.08%, compared to the existing codes’ limitations. Comparative analyses highlight the accuracy of the empirical equation, revealing an improved convergence of data points and minimal sensitivity to variations in key parameters. The results proved that the proposed empirical equation enhanced the accuracy to predict the shear strength capacity of the reinforced concrete deep beams in various scenarios, making it a valuable tool for structural engineers. This research contributes to advancing the understanding of shear strength capacity in reinforced concrete deep beams, offering a reliable empirical equation with implications for refining design methodologies and enhancing safety with the efficiency of structural systems.

Modelling fuel oil transformation on geographically different seacoasts and assessing their self-cleansing capacity.

The present paper considers the results of long-term (up to 17years) in situ and laboratory research carried out on oiled French, Spanish, and Russian seacoasts. The objective of this research is to quantify the influence of geographical factors on the rates of natural transformation of the heavy fuel oil stranded ashore and to develop an empirical statistical model in order to evaluate the self-cleansing capacity of the coastal environment. In a number of field campaigns, 363 samples of weathered oil slicks and tar balls have been collected and analysed with the use of thin-layer chromatography combined with optical and gravimetric methods. The results obtained have been subjected to multiple nonlinear regression analyses. It has been shown that heavy fuel oil natural attenuation is more active in continental or estuarine environments influenced by nutrient-rich freshwater runoff and characterised by a higher number of sunny days, solar irradiation, and large temperature fluctuations. On the oceanic coasts, especially in sectors with low hydrodynamic energy, these processes take more time. The resulting model allows for the identification and mapping of the most vulnerable seacoasts, characterised by a low potential to degrade oil pollution. This information may be used in the contingency plans in order to optimise clean-up techniques and associated costs.